一种基于独立成分指纹和支持向量机的海员心理状况评估方法

A method based on independent component and support vector machine for the evaluation of mental status of sailors

目的：海员作为一种特殊的职业群体，其心理健康与否对整个航海过程的安危至关重要。本研究提出了一种用独立成分（ independent component ， IC ）指纹和支持向量机（ support vector machine， SVM）相结合评估海员心理状况的方法。方法首先基于健康对照组中100名受试者与海员组中88名受试者的静息状态默认网络的IC指纹特征，利用双重SVM建海员心理健康评估模型，然后利用训练出的评估模型对88名海员的心理状况进行评测，检测出心理存在异常的海员。结果从88名海员中检测出5名海员心理存在异常；进一步比较该5名心理异常海员的默认网络和健康对照组的默认网络，发现的确存在明显的不同。结论以默认网络的偏度、斜度、空间信息熵、聚类度、一步延迟自相关、时间信息熵和频率能量分布等为特征，利用双重支持向量机构建海员心理健康评估模型，能够有效地检测出心理异常的海员，该研究为今后海员心理健康的评测提供了有力的客观依据。

Objective To investigate a method based on independent component （ IC） and support vector machine （ SVM） for the detection of abnormal mental status of sailors .Methods First, the features of fingerprints were identified in 100 subjects from the control group and 88 subjects from the sailor group , and the twin support vector machine was used to establish the model for mental health evaluation .Then, the derrived evaluation model was used to evaluate the mental status of 88 sailors and identify those sailors with menatal disorder .Results Our research revealed that 5 out of 88 sailors were detected to be mentally abnormal .Further comparison of the default mode network between 5 abnormal sailors and that of the normal subjects revealed that there were significant differences between them .Conclusions With the prominent features of kurtosis , skewness, spatial entropy, degree of clustering, one-lag serial autocorrelation, temporal entropy and power contribution of the default mode network , the twin support vector machine could be used to establish the mental health evaluation model for the detection of those sailors with mental disorder .The research results could provide good objective standards for future evaluation of the mental health in sailors .